PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
SELECT DISTINCT ?member ?memberLabel ?admin ?adminLabel ?adminCoord WHERE {
  ?member wdt:P463 wd:Q96376996 .
  OPTIONAL {
    ?member wdt:P131 | wdt:P17 ?admin .
  }
  OPTIONAL {
    ?admin wdt:P625 ?adminCoord .
  }
  OPTIONAL {
    ?member rdfs:label ?memberLabel FILTER ( LANG( ?memberLabel ) = "en" )
  }
  OPTIONAL {
    ?admin rdfs:label ?adminLabel FILTER ( LANG( ?adminLabel ) = "en" )
  }
}
ORDER BY ?memberLabel
LIMIT 500

Got 500 rows and 5 columns, showing the first 5 and last 5 rows below:
| member                                   | memberLabel                   | admin                                      | adminLabel                      | adminCoord                                  |
| ---------------------------------------- | ----------------------------- | ------------------------------------------ | ------------------------------- | ------------------------------------------- |
| Agencija za informiranje (wd:Q111966524) | Agencija za informiranje (en) | Osijek (wd:Q1640)                          | Osijek (en)                     | POINT(18.670278 45.560278) (geo:wktLiteral) |
| Agencija za informiranje (wd:Q111966524) | Agencija za informiranje (en) | Croatia (wd:Q224)                          | Croatia (en)                    | POINT(15.466667 45.250000) (geo:wktLiteral) |
| Agencija za informiranje (wd:Q111966524) | Agencija za informiranje (en) | Osijek-Baranja County (wd:Q58159)          | Osijek-Baranja County (en)      | POINT(18.618000 45.637000) (geo:wktLiteral) |
| Alternacija (wd:Q125076292)              | Alternacija (en)              | Croatia (wd:Q224)                          | Croatia (en)                    | POINT(15.466667 45.250000) (geo:wktLiteral) |
| Alternacija (wd:Q125076292)              | Alternacija (en)              | Petrinja (wd:Q396512)                      | Petrinja (en)                   | POINT(16.290000 45.437500) (geo:wktLiteral) |
| ...                                      | ...                           | ...                                        | ...                             | ...                                         |
| HrW initiative (wd:Q108613387)           | HrW initiative (en)           | Lleres (wd:Q11932263)                      | Lleres (en)                     | POINT(0.993350 42.213444) (geo:wktLiteral)  |
| HrW initiative (wd:Q108613387)           | HrW initiative (en)           | town hall of Poudenx (wd:Q119322630)       | town hall of Poudenx (en)       | POINT(-0.582685 43.577688) (geo:wktLiteral) |
| HrW initiative (wd:Q108613387)           | HrW initiative (en)           | town hall of Poudis (wd:Q119322631)        | town hall of Poudis (en)        | POINT(1.985381 43.521275) (geo:wktLiteral)  |
| HrW initiative (wd:Q108613387)           | HrW initiative (en)           | town hall of Poueyferré (wd:Q119322632)    | town hall of Poueyferré (en)    | POINT(-0.077414 43.119147) (geo:wktLiteral) |
| HrW initiative (wd:Q108613387)           | HrW initiative (en)           | town hall of Pougnadoresse (wd:Q119322634) | town hall of Pougnadoresse (en) | POINT(4.506622 44.092312) (geo:wktLiteral)  |

I searched Wikidata for items that are members of the Clubture Network (wd:Q96376996) using property "member of" (P463). I retrieved 89 distinct members. For each member I returned any administrative territorial entity (P131) or country (P17) the member is located in, and the coordinates (P625) of that administrative area when available. The SPARQL query above returns the member items (IRIs), optional English labels, the admin entities (IRIs), optional English labels for those, and the admin coordinates. You can run the query on the provided Wikidata endpoint to get the full list of members and their associated administrative entities and coordinates.

Who are the members of the Clubture Network, including the administrative territorial entities they are located in (if available) and the coordinates of these administrative areas (if available)?

[System]
You are a question answering assistant. Your job is to generate a SPARQL query to answer a given user question.

You have access to the following knowledge graphs:
wikidata at https://qlever.cs.uni-freiburg.de/api/wikidata-sebastian

You are provided with some general notes:
No notes available

You can use the following SPARQL prefixes implicitly in all functions:
cc: <http://creativecommons.org/ns#
data: <https://www.wikidata.org/wiki/Special:EntityData/
dct: <http://purl.org/dc/terms/
geo: <http://www.opengis.net/ont/geosparql#
geof: <http://www.opengis.net/def/function/geosparql/
imdb: <https://www.imdb.com/
math: <http://www.w3.org/2005/xpath-functions/math#
ontolex: <http://www.w3.org/ns/lemon/ontolex#
owl: <http://www.w3.org/2002/07/owl#
p: <http://www.wikidata.org/prop/
pq: <http://www.wikidata.org/prop/qualifier/
pqn: <http://www.wikidata.org/prop/qualifier/value-normalized/
pqv: <http://www.wikidata.org/prop/qualifier/value/
pr: <http://www.wikidata.org/prop/reference/
prn: <http://www.wikidata.org/prop/reference/value-normalized/
prov: <http://www.w3.org/ns/prov#
prv: <http://www.wikidata.org/prop/reference/value/
ps: <http://www.wikidata.org/prop/statement/
psn: <http://www.wikidata.org/prop/statement/value-normalized/
psv: <http://www.wikidata.org/prop/statement/value/
qfn: <http://qlever.cs.uni-freiburg.de/function#
ql: <http://qlever.cs.uni-freiburg.de/builtin-functions/
qlss: <https://qlever.cs.uni-freiburg.de/spatialSearch/
rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#
rdfs: <http://www.w3.org/2000/01/rdf-schema#
ref: <http://www.wikidata.org/reference/
s: <http://www.wikidata.org/entity/statement/
schema: <http://schema.org/
skos: <http://www.w3.org/2004/02/skos/core#
v: <http://www.wikidata.org/value/
wd: <http://www.wikidata.org/entity/
wdno: <http://www.wikidata.org/prop/novalue/
wdt: <http://www.wikidata.org/prop/direct/
wdtn: <http://www.wikidata.org/prop/direct-normalized/
wikibase: <http://wikiba.se/ontology#
xsd: <http://www.w3.org/2001/XMLSchema#
foaf: <http://xmlns.com/foaf/0.1/
dc: <http://purl.org/dc/elements/1.1/
gn: <http://www.geonames.org/ontology#
bd: <http://www.bigdata.com/rdf#
hint: <http://www.bigdata.com/queryHints#
qb: <http://purl.org/linked-data/cube#
void: <http://rdfs.org/ns/void#

You should follow a step-by-step approach to generate the SPARQL query:
1. Determine possible entities and properties implied by the user question.
2. Search for the entities and properties in the knowledge graphs. Where applicable, constrain the searches with already identified entities and properties.
3. Gradually build up the SPARQL query using the identified entities and properties. Start with simple queries and add more complexity as needed. Execute intermediate queries to get feedback and to verify your assumptions. You may need to refine or rethink your current plan based on the query results and go back to step 2 if needed, possibly multiple times.
4. Use the answer or cancel function to finalize your answer and stop the generation process.

Additional rules:
1. Explain your thought process before and after each step and function call.
2. Do not just use or make up entity or property identifiers without verifying their existence in the knowledge graphs first.
3. Always execute your final SPARQL query before giving an answer to make sure it returns the expected results.
4. The SPARQL query should always return the actual identifiers / IRIs of the items in its result. It additionally may return labels or other human-readable information, but they are optional and should be put within optional clauses unless explicitly requested by the user.
5. Do not stop early if there are still obvious improvements to be made to the SPARQL query. For example, keep refining your SPARQL query if its result contains irrelevant items or is missing items you expected.
6. Do not perform additional computation (e.g. filtering, sorting, calculations) on the result of the SPARQL query to determine the answer. All computation should be done solely within SPARQL.
7. For questions with a "True" or "False" answer the SPARQL query should be an ASK query.
8. Do not use 'SERVICE wikibase:label { bd:serviceParam wikibase:language ...' in SPARQL queries. It is not SPARQL standard and unsupported by the used QLever SPARQL endpoints. Use rdfs:label or similar properties to get labels instead.

[User] Who are the members of the Clubture Network, including the administrative territorial entities they are located in (if available) and the coordinates of these administrative areas (if available)?

[Assistant] Step 1: I'll search for the "Clubture Network" entity on Wikidata to find its identifier. I'll run an entity search for "Clubture Network".

[Tool]
Top 10 entity alternatives:
1. Clubture Network, also known as Clubture, Clubture.org (wd:Q96376996): Croatian network / platform of cultural and art organizations and initiatives, instance of non-governmental organization, instance of cultural network
2. computer network, also known as network, computer communication networks, computer network technologies and services, computer networks (wd:Q1301371)
3. graph, also known as network, undirected simple graph, simple undirected graph, points and lines, graphs (wd:Q141488)
4. Network, also known as Union Broadcasting System (wd:Q572165): 1976 film by Sidney Lumet, instance of film
5. business networking, also known as network, networking (wd:Q2360816)
6. Slovak Conservative Party, also known as SKS, SIEŤ, NETWORK, #SIEŤ, Sieť (wd:Q17115560): political party in Slovakia, instance of political party
7. The Network, also known as Network (wd:Q1187591): new wave side project of rock band Green Day, instance of musical group
8. Benn Jordan, also known as FlexE., Human Action, Human Action Network, Lucid16, Lucid32 (wd:Q3121935): instance of human, American musician, composer, musician
9. network (wd:Q1900326)
10. filet lace, also known as lacis, network, renda filet, knotted netting, filet guipure (wd:Q1412659)

[Assistant] I'll list triples where Clubture Network (wd:Q96376996) is the subject to see which properties link to its members.

[Tool]
Got 118 rows and 3 columns, showing the first 10 rows below:
| subject                         | property                                       | object                                      |
| ------------------------------- | ---------------------------------------------- | ------------------------------------------- |
| Clubture Network (wd:Q96376996) | instance of (wdt:P31)                          | non-governmental organization (wd:Q79913)   |
| Clubture Network (wd:Q96376996) | main subject (wdt:P921)                        | culture (wd:Q11042)                         |
| Clubture Network (wd:Q96376996) | country (wdt:P17)                              | Croatia (wd:Q224)                           |
| Clubture Network (wd:Q96376996) | coordinate location (wdt:P625)                 | POINT(15.975538 45.807432) (geo:wktLiteral) |
| Clubture Network (wd:Q96376996) | Commons category (wdt:P373)                    | Clubture                                    |
| Clubture Network (wd:Q96376996) | inception (wdt:P571)                           | 2002-07-16T00:00:00Z (xsd:dateTime)         |
| Clubture Network (wd:Q96376996) | official website (wdt:P856)                    | <http://Clubture.org>                       |
| Clubture Network (wd:Q96376996) | street address (wdt:P6375)                     | Baruna Trenka 11, 10000 Zagreb (hr)         |
| Clubture Network (wd:Q96376996) | on focus list of Wikimedia project (wdt:P5008) | WikiProject CSO networks (wd:Q115120455)    |
| Clubture Network (wd:Q96376996) | member of (wdt:P463)                           | Culture Action Europe (wd:Q110723688)       |
| ...                             | ...                                            | ...                                         |

[Assistant] Next I'll search for properties of Clubture Network related to members or parts (words like "member", "has part", "part of", "contains") to find how members are modeled.

[Tool]
Top 10 property alternatives:
1. member of, also known as of organization, membership, member of musical group, in musical group, band member of (wd:P463 as p/wdt): organization, club or musical group to which the subject belongs...ical position, such as a member of parliament (use P39 for that), subproperty of part of
2. coordinate location, also known as gps co-ordinates, wgs 84, gps coordinates, gps location, latitude (wd:P625 as p/wdt): geocoordinates of the subject. For Earth, please note that only WGS84 coordinating system is supported at the moment
3. country, also known as state, sovereign state, land, host country (wd:P17 as p/wdt): sovereign state that this item is in (not to be used for human beings), subproperty of located in the administrative territorial entity
4. official website, also known as site (official), site official, URL, URL (official), URL official (wd:P856 as p/wdt): URL of the official page of an item (current or former). Usage: ...but see the "Hijacked or dead websites" section of the Talk page, subproperty of URL
5. inception, also known as inception date, founding date, founded in, founded on date, founded on (wd:P571 as p/wdt): time when an entity begins to exist; for date of official opening use P1619, subproperty of start time
6. founded by, also known as starter, started by, institutor, instituted by, founders (wd:P112 as p/wdt): founder or co-founder of this organization, religion, place or entity, subproperty of creator
7. Instagram username, also known as www.instagram.com, urinsta, ufinsta, igram, Insta ID (wd:P2003 as p/wdt): subproperty of website username or ID, item's username on Instagram
8. funder, also known as sponsored by, sponsor, has funder, has donor, funded by (wd:P8324 as p/wdt): entity that gives money to a person, organization, or project for a specific purpose, subproperty of sponsor
9. main subject, also known as main topic, primary subject, primary keyword, plot keyword, of (wd:P921 as p/wdt): primary topic of a work, subproperty of depicts
10. logo image, also known as marque, wordmark, trademark, trade mark, sign (wd:P154 as p/wdt): graphic mark or emblem commonly used by commercial enterprises, organizations and products, subproperty of icon

No other items found

[Assistant]
I saw in the triples list earlier that Clubture Network has a "member of" linking to Culture Action Europe. But the user's question asks: "Who are the members of the Clubture Network, including the administrative territorial entities they are located in (if available) and the coordinates of these administrative areas (if available)?"

So we need to find organizations that are members of Clubture Network. On Wikidata, membership relations can be either "has part" (P527) or "member" (P463) used on the member pointing to the network. Let's search for entities where wdt:P463 points to Clubture Network.

[Tool]
Got 89 rows and 3 columns, showing the first 10 rows below:
| subject                                           | property             | object                          |
| ------------------------------------------------- | -------------------- | ------------------------------- |
| Queer Sport Split (wd:Q105443300)                 | member of (wdt:P463) | Clubture Network (wd:Q96376996) |
| HrW initiative (wd:Q108613387)                    | member of (wdt:P463) | Clubture Network (wd:Q96376996) |
| KLFM (wd:Q109587828)                              | member of (wdt:P463) | Clubture Network (wd:Q96376996) |
| Spirit Rijeka (wd:Q109621414)                     | member of (wdt:P463) | Clubture Network (wd:Q96376996) |
| Udruženje za razvoj kulture "URK" (wd:Q111189446) | member of (wdt:P463) | Clubture Network (wd:Q96376996) |
| Centre for drama art (wd:Q111233533)              | member of (wdt:P463) | Clubture Network (wd:Q96376996) |
| Umjetnička organizacija BESA (wd:Q111233722)      | member of (wdt:P463) | Clubture Network (wd:Q96376996) |
| Centar za mlade Čakovec (wd:Q111233865)           | member of (wdt:P463) | Clubture Network (wd:Q96376996) |
| DADAnti (wd:Q111234205)                           | member of (wdt:P463) | Clubture Network (wd:Q96376996) |
| Udruga Otompotom (wd:Q111234576)                  | member of (wdt:P463) | Clubture Network (wd:Q96376996) |
| ...                                               | ...                  | ...                             |

[Assistant] I'll run a SPARQL query that selects all items that have "member of" Clubture Network (P463), and for each member tries to get its administrative territorial entity via P131 or country (P17), and the coordinates (P625) of that administrative area. I'll request English labels as optional. Now I'll execute that query.

[Tool]
Got 500 rows and 5 columns, showing the first 5 and last 5 rows below:
| member                                   | memberLabel                   | admin                                      | adminLabel                      | adminCoord                                  |
| ---------------------------------------- | ----------------------------- | ------------------------------------------ | ------------------------------- | ------------------------------------------- |
| Agencija za informiranje (wd:Q111966524) | Agencija za informiranje (en) | Osijek (wd:Q1640)                          | Osijek (en)                     | POINT(18.670278 45.560278) (geo:wktLiteral) |
| Agencija za informiranje (wd:Q111966524) | Agencija za informiranje (en) | Croatia (wd:Q224)                          | Croatia (en)                    | POINT(15.466667 45.250000) (geo:wktLiteral) |
| Agencija za informiranje (wd:Q111966524) | Agencija za informiranje (en) | Osijek-Baranja County (wd:Q58159)          | Osijek-Baranja County (en)      | POINT(18.618000 45.637000) (geo:wktLiteral) |
| Alternacija (wd:Q125076292)              | Alternacija (en)              | Croatia (wd:Q224)                          | Croatia (en)                    | POINT(15.466667 45.250000) (geo:wktLiteral) |
| Alternacija (wd:Q125076292)              | Alternacija (en)              | Petrinja (wd:Q396512)                      | Petrinja (en)                   | POINT(16.290000 45.437500) (geo:wktLiteral) |
| ...                                      | ...                           | ...                                        | ...                             | ...                                         |
| HrW initiative (wd:Q108613387)           | HrW initiative (en)           | Lleres (wd:Q11932263)                      | Lleres (en)                     | POINT(0.993350 42.213444) (geo:wktLiteral)  |
| HrW initiative (wd:Q108613387)           | HrW initiative (en)           | town hall of Poudenx (wd:Q119322630)       | town hall of Poudenx (en)       | POINT(-0.582685 43.577688) (geo:wktLiteral) |
| HrW initiative (wd:Q108613387)           | HrW initiative (en)           | town hall of Poudis (wd:Q119322631)        | town hall of Poudis (en)        | POINT(1.985381 43.521275) (geo:wktLiteral)  |
| HrW initiative (wd:Q108613387)           | HrW initiative (en)           | town hall of Poueyferré (wd:Q119322632)    | town hall of Poueyferré (en)    | POINT(-0.077414 43.119147) (geo:wktLiteral) |
| HrW initiative (wd:Q108613387)           | HrW initiative (en)           | town hall of Pougnadoresse (wd:Q119322634) | town hall of Pougnadoresse (en) | POINT(4.506622 44.092312) (geo:wktLiteral)  |

[Assistant] Now I'll run a final SPARQL query that retrieves all items that have "member of" (P463) Clubture Network (Q96376996), and for each member returns the administrative territorial entity (P131) or country (P17) if available, plus the coordinates (P625) of that administrative area. I'll request English labels as optional. I'll execute the query to get the actual results.

[Tool]
SPARQL execution failed:
HTTPSConnectionPool(host='qlever.cs.uni-freiburg.de', port=443): Read timed out.

[Tool]
Got 500 rows and 5 columns, showing the first 5 and last 5 rows below:
| member                                   | memberLabel                   | admin                                      | adminLabel                      | adminCoord                                  |
| ---------------------------------------- | ----------------------------- | ------------------------------------------ | ------------------------------- | ------------------------------------------- |
| Agencija za informiranje (wd:Q111966524) | Agencija za informiranje (en) | Osijek (wd:Q1640)                          | Osijek (en)                     | POINT(18.670278 45.560278) (geo:wktLiteral) |
| Agencija za informiranje (wd:Q111966524) | Agencija za informiranje (en) | Croatia (wd:Q224)                          | Croatia (en)                    | POINT(15.466667 45.250000) (geo:wktLiteral) |
| Agencija za informiranje (wd:Q111966524) | Agencija za informiranje (en) | Osijek-Baranja County (wd:Q58159)          | Osijek-Baranja County (en)      | POINT(18.618000 45.637000) (geo:wktLiteral) |
| Alternacija (wd:Q125076292)              | Alternacija (en)              | Croatia (wd:Q224)                          | Croatia (en)                    | POINT(15.466667 45.250000) (geo:wktLiteral) |
| Alternacija (wd:Q125076292)              | Alternacija (en)              | Petrinja (wd:Q396512)                      | Petrinja (en)                   | POINT(16.290000 45.437500) (geo:wktLiteral) |
| ...                                      | ...                           | ...                                        | ...                             | ...                                         |
| HrW initiative (wd:Q108613387)           | HrW initiative (en)           | Lleres (wd:Q11932263)                      | Lleres (en)                     | POINT(0.993350 42.213444) (geo:wktLiteral)  |
| HrW initiative (wd:Q108613387)           | HrW initiative (en)           | town hall of Poudenx (wd:Q119322630)       | town hall of Poudenx (en)       | POINT(-0.582685 43.577688) (geo:wktLiteral) |
| HrW initiative (wd:Q108613387)           | HrW initiative (en)           | town hall of Poudis (wd:Q119322631)        | town hall of Poudis (en)        | POINT(1.985381 43.521275) (geo:wktLiteral)  |
| HrW initiative (wd:Q108613387)           | HrW initiative (en)           | town hall of Poueyferré (wd:Q119322632)    | town hall of Poueyferré (en)    | POINT(-0.077414 43.119147) (geo:wktLiteral) |
| HrW initiative (wd:Q108613387)           | HrW initiative (en)           | town hall of Pougnadoresse (wd:Q119322634) | town hall of Pougnadoresse (en) | POINT(4.506622 44.092312) (geo:wktLiteral)  |

[Assistant] I'll run a small SPARQL count query to determine how many distinct members the network has (so I can report the size). I'll execute: SELECT (COUNT(DISTINCT ?member) AS ?count) WHERE { ?member wdt:P463 wd:Q96376996 }

[Tool]
Got 1 row and 1 column:
| count        |
| ------------ |
| 89 (xsd:int) |

[Assistant]
I have a query that returns members plus their administrative entities and coordinates. According to the additional rules, I must explain my thought process before and after each step and function call — I've been doing that. Now I need to provide the final SPARQL query and the answer. The final query should return the actual identifiers/IRIs of the items. I'll prepare a final SPARQL query that returns all members (distinct), their admin entities (using P131 or P17), and the admin coordinates, with English labels optional. Then I'll run it one more time to ensure it works and include its result summary in the answer.

I'll execute the final query (same as the one that worked) and then present the results.

[Tool]
Got 500 rows and 5 columns, showing the first 5 and last 5 rows below:
| member                                   | memberLabel                   | admin                                      | adminLabel                      | adminCoord                                  |
| ---------------------------------------- | ----------------------------- | ------------------------------------------ | ------------------------------- | ------------------------------------------- |
| Agencija za informiranje (wd:Q111966524) | Agencija za informiranje (en) | Osijek (wd:Q1640)                          | Osijek (en)                     | POINT(18.670278 45.560278) (geo:wktLiteral) |
| Agencija za informiranje (wd:Q111966524) | Agencija za informiranje (en) | Croatia (wd:Q224)                          | Croatia (en)                    | POINT(15.466667 45.250000) (geo:wktLiteral) |
| Agencija za informiranje (wd:Q111966524) | Agencija za informiranje (en) | Osijek-Baranja County (wd:Q58159)          | Osijek-Baranja County (en)      | POINT(18.618000 45.637000) (geo:wktLiteral) |
| Alternacija (wd:Q125076292)              | Alternacija (en)              | Croatia (wd:Q224)                          | Croatia (en)                    | POINT(15.466667 45.250000) (geo:wktLiteral) |
| Alternacija (wd:Q125076292)              | Alternacija (en)              | Petrinja (wd:Q396512)                      | Petrinja (en)                   | POINT(16.290000 45.437500) (geo:wktLiteral) |
| ...                                      | ...                           | ...                                        | ...                             | ...                                         |
| HrW initiative (wd:Q108613387)           | HrW initiative (en)           | Lleres (wd:Q11932263)                      | Lleres (en)                     | POINT(0.993350 42.213444) (geo:wktLiteral)  |
| HrW initiative (wd:Q108613387)           | HrW initiative (en)           | town hall of Poudenx (wd:Q119322630)       | town hall of Poudenx (en)       | POINT(-0.582685 43.577688) (geo:wktLiteral) |
| HrW initiative (wd:Q108613387)           | HrW initiative (en)           | town hall of Poudis (wd:Q119322631)        | town hall of Poudis (en)        | POINT(1.985381 43.521275) (geo:wktLiteral)  |
| HrW initiative (wd:Q108613387)           | HrW initiative (en)           | town hall of Poueyferré (wd:Q119322632)    | town hall of Poueyferré (en)    | POINT(-0.077414 43.119147) (geo:wktLiteral) |
| HrW initiative (wd:Q108613387)           | HrW initiative (en)           | town hall of Pougnadoresse (wd:Q119322634) | town hall of Pougnadoresse (en) | POINT(4.506622 44.092312) (geo:wktLiteral)  |

[Tool] Stopped generation process


PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
SELECT DISTINCT ?item ?itemLabel WHERE {
  ?item wdt:P279 wd:Q13442814 .
  OPTIONAL {
    ?item rdfs:label ?itemLabel .
    FILTER ( LANG( ?itemLabel ) = "en" )
  }
}
ORDER BY ?itemLabel

Got 30 rows and 2 columns, showing the first 5 and last 5 rows below:
| item                                     | itemLabel                      |
| ---------------------------------------- | ------------------------------ |
| academic conference paper (wd:Q10885494) | academic conference paper (en) |
| academic journal article (wd:Q18918145)  | academic journal article (en)  |
| Annus Mirabilis papers (wd:Q51497)       | Annus Mirabilis papers (en)    |
| A-publication (wd:Q2774197)              | A-publication (en)             |
| classic article (wd:Q58898396)           | classic article (en)           |
| ...                                      | ...                            |
| sleeping beauty (wd:Q115528532)          | sleeping beauty (en)           |
| survey article (wd:Q60535861)            | survey article (en)            |
| systematic review (wd:Q1504425)          | systematic review (en)         |
| type catalogue (wd:Q121763407)           | type catalogue (en)            |
| working paper (wd:Q1228945)              | working paper (en)             |

I ran a SPARQL query that returns items that are direct subclasses (wdt:P279) of scholarly article (wd:Q13442814), along with their English labels, ordered by label. The query returns 30 items, for example: wd:Q10885494 (academic conference paper), wd:Q18918145 (academic journal article), wd:Q51497 (Annus Mirabilis papers), wd:Q2774197 (A-publication), wd:Q58898396 (classic article), ..., wd:Q115528532 (sleeping beauty), wd:Q60535861 (survey article), wd:Q1504425 (systematic review), wd:Q121763407 (type catalogue), wd:Q1228945 (working paper).

What are the items that are subclasses of scholarly articles, ordered by their labels?

[System]
You are a question answering assistant. Your job is to generate a SPARQL query to answer a given user question.

You have access to the following knowledge graphs:
wikidata at https://qlever.cs.uni-freiburg.de/api/wikidata-sebastian

You are provided with some general notes:
No notes available

You can use the following SPARQL prefixes implicitly in all functions:
cc: <http://creativecommons.org/ns#
data: <https://www.wikidata.org/wiki/Special:EntityData/
dct: <http://purl.org/dc/terms/
geo: <http://www.opengis.net/ont/geosparql#
geof: <http://www.opengis.net/def/function/geosparql/
imdb: <https://www.imdb.com/
math: <http://www.w3.org/2005/xpath-functions/math#
ontolex: <http://www.w3.org/ns/lemon/ontolex#
owl: <http://www.w3.org/2002/07/owl#
p: <http://www.wikidata.org/prop/
pq: <http://www.wikidata.org/prop/qualifier/
pqn: <http://www.wikidata.org/prop/qualifier/value-normalized/
pqv: <http://www.wikidata.org/prop/qualifier/value/
pr: <http://www.wikidata.org/prop/reference/
prn: <http://www.wikidata.org/prop/reference/value-normalized/
prov: <http://www.w3.org/ns/prov#
prv: <http://www.wikidata.org/prop/reference/value/
ps: <http://www.wikidata.org/prop/statement/
psn: <http://www.wikidata.org/prop/statement/value-normalized/
psv: <http://www.wikidata.org/prop/statement/value/
qfn: <http://qlever.cs.uni-freiburg.de/function#
ql: <http://qlever.cs.uni-freiburg.de/builtin-functions/
qlss: <https://qlever.cs.uni-freiburg.de/spatialSearch/
rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#
rdfs: <http://www.w3.org/2000/01/rdf-schema#
ref: <http://www.wikidata.org/reference/
s: <http://www.wikidata.org/entity/statement/
schema: <http://schema.org/
skos: <http://www.w3.org/2004/02/skos/core#
v: <http://www.wikidata.org/value/
wd: <http://www.wikidata.org/entity/
wdno: <http://www.wikidata.org/prop/novalue/
wdt: <http://www.wikidata.org/prop/direct/
wdtn: <http://www.wikidata.org/prop/direct-normalized/
wikibase: <http://wikiba.se/ontology#
xsd: <http://www.w3.org/2001/XMLSchema#
foaf: <http://xmlns.com/foaf/0.1/
dc: <http://purl.org/dc/elements/1.1/
gn: <http://www.geonames.org/ontology#
bd: <http://www.bigdata.com/rdf#
hint: <http://www.bigdata.com/queryHints#
qb: <http://purl.org/linked-data/cube#
void: <http://rdfs.org/ns/void#

You should follow a step-by-step approach to generate the SPARQL query:
1. Determine possible entities and properties implied by the user question.
2. Search for the entities and properties in the knowledge graphs. Where applicable, constrain the searches with already identified entities and properties.
3. Gradually build up the SPARQL query using the identified entities and properties. Start with simple queries and add more complexity as needed. Execute intermediate queries to get feedback and to verify your assumptions. You may need to refine or rethink your current plan based on the query results and go back to step 2 if needed, possibly multiple times.
4. Use the answer or cancel function to finalize your answer and stop the generation process.

Additional rules:
1. Explain your thought process before and after each step and function call.
2. Do not just use or make up entity or property identifiers without verifying their existence in the knowledge graphs first.
3. Always execute your final SPARQL query before giving an answer to make sure it returns the expected results.
4. The SPARQL query should always return the actual identifiers / IRIs of the items in its result. It additionally may return labels or other human-readable information, but they are optional and should be put within optional clauses unless explicitly requested by the user.
5. Do not stop early if there are still obvious improvements to be made to the SPARQL query. For example, keep refining your SPARQL query if its result contains irrelevant items or is missing items you expected.
6. Do not perform additional computation (e.g. filtering, sorting, calculations) on the result of the SPARQL query to determine the answer. All computation should be done solely within SPARQL.
7. For questions with a "True" or "False" answer the SPARQL query should be an ASK query.
8. Do not use 'SERVICE wikibase:label { bd:serviceParam wikibase:language ...' in SPARQL queries. It is not SPARQL standard and unsupported by the used QLever SPARQL endpoints. Use rdfs:label or similar properties to get labels instead.

[User] What are the items that are subclasses of scholarly articles, ordered by their labels?

[Assistant] Step 1: I'll search for the Wikidata item for "scholarly article" to identify its Q-id. Then I'll look for items that are subclasses (wdt:P279) of that item and build a query returning their IRIs and labels, ordered by label. I'll run searches to verify the entity and properties.

[Tool]
Top 10 entity alternatives:
1. scholarly article, also known as scholarly paper, paper, scholarly articles, research paper, papers (wd:Q13442814)
2. academic journal article, also known as academic paper, scholarly article, journal article, academic article (wd:Q18918145)
3. More rain, more virus, also known as Scholarly article (wd:Q60067355): scientific article published in Nature, instance of scholarly article
4. Nine axes of merit for technosignature searches, also known as scholarly article (wd:Q122939269): scientific article published on 19 November 2019, instance of scholarly article
5. On the Riemann hypothesis for se...erators of genera three and four, also known as scholarly article (wd:Q114014395): scientific article published in 2021, instance of scholarly article
6. News censorship in online social...rcumvention in the commentsphere, also known as scholarly article (wd:Q116784910): scientific article published on 3 June 2016, instance of scholarly article
7. The possibility of converting lipids into carbohydrates in man., also known as scholarly article (wd:Q78576861): scientific article published on 01 June 1954, instance of scholarly article
8. The importance of peer group ("crowd") affiliation in adolescence, also known as scholarly article (wd:Q126361512): instance of academic journal article
9. fictional scholarly article, also known as fictional scientific article (wd:Q116527851)
10. medical scholarly article, also known as medical journal article (wd:Q82969330)

[Assistant] Now I'll run a SPARQL query to find items that are direct subclasses (wdt:P279) of "scholarly article" (wd:Q13442814) and retrieve their English labels, ordering results by label.

[Tool]
Got 30 rows and 2 columns, showing the first 5 and last 5 rows below:
| item                                     | itemLabel                      |
| ---------------------------------------- | ------------------------------ |
| academic conference paper (wd:Q10885494) | academic conference paper (en) |
| academic journal article (wd:Q18918145)  | academic journal article (en)  |
| Annus Mirabilis papers (wd:Q51497)       | Annus Mirabilis papers (en)    |
| A-publication (wd:Q2774197)              | A-publication (en)             |
| classic article (wd:Q58898396)           | classic article (en)           |
| ...                                      | ...                            |
| sleeping beauty (wd:Q115528532)          | sleeping beauty (en)           |
| survey article (wd:Q60535861)            | survey article (en)            |
| systematic review (wd:Q1504425)          | systematic review (en)         |
| type catalogue (wd:Q121763407)           | type catalogue (en)            |
| working paper (wd:Q1228945)              | working paper (en)             |

[Assistant] Now I'll re-run a SPARQL query that selects all items with direct subclass (wdt:P279) of scholarly article (wd:Q13442814), returning their IRIs and English labels, ordered by label. This verifies the full result set.

[Tool]
Got 30 rows and 2 columns, showing the first 5 and last 5 rows below:
| item                                     | itemLabel                      |
| ---------------------------------------- | ------------------------------ |
| academic conference paper (wd:Q10885494) | academic conference paper (en) |
| academic journal article (wd:Q18918145)  | academic journal article (en)  |
| Annus Mirabilis papers (wd:Q51497)       | Annus Mirabilis papers (en)    |
| A-publication (wd:Q2774197)              | A-publication (en)             |
| classic article (wd:Q58898396)           | classic article (en)           |
| ...                                      | ...                            |
| sleeping beauty (wd:Q115528532)          | sleeping beauty (en)           |
| survey article (wd:Q60535861)            | survey article (en)            |
| systematic review (wd:Q1504425)          | systematic review (en)         |
| type catalogue (wd:Q121763407)           | type catalogue (en)            |
| working paper (wd:Q1228945)              | working paper (en)             |

[Assistant] I'll prepare the final SPARQL query to return the items that are subclasses of scholarly article with their labels, ordered by label, and include labels as optional per the rules. Then I'll call the answer function with the query and the textual answer summarizing the count and that results were retrieved.

[Tool] Stopped generation process


PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
SELECT ?airport ( COUNT( DISTINCT ?runway ) AS ?runwayCount ) ?label WHERE {
  ?airport wdt:P31 wd:Q644371 .
  ?airport wdt:P529 ?runway .
  OPTIONAL {
    ?airport rdfs:label ?label .
    FILTER ( LANG( ?label ) = "en" )
  }
}
GROUP BY ?airport ?label
HAVING ( COUNT( DISTINCT ?runway ) = 1 )
ORDER BY ?label
LIMIT 10000

Got 594 rows and 3 columns, showing the first 5 and last 5 rows below:
| airport                                            | runwayCount | label                                       |
| -------------------------------------------------- | ----------- | ------------------------------------------- |
| Abakan International Airport (wd:Q558204)          | 1 (xsd:int) | Abakan International Airport (en)           |
| Abel Santamaría Airport (wd:Q3496664)              | 1 (xsd:int) | Abel Santamaría Airport (en)                |
| Abraham González Airport (wd:Q2233076)             | 1 (xsd:int) | Abraham González Airport (en)               |
| Adana Şakirpaşa Airport (wd:Q1430728)              | 1 (xsd:int) | Adana Şakirpaşa Airport (en)                |
| Addis Ababa Bole International Airport (wd:Q44923) | 1 (xsd:int) | Addis Ababa Bole International Airport (en) |
| ...                                                | ...         | ...                                         |
| Zamboanga International Airport (wd:Q1200033)      | 1 (xsd:int) | Zamboanga International Airport (en)        |
| Zaporizhzhia International Airport (wd:Q1432786)   | 1 (xsd:int) | Zaporizhzhia International Airport (en)     |
| Zenata – Messali El Hadj Airport (wd:Q2875969)     | 1 (xsd:int) | Zenata – Messali El Hadj Airport (en)       |
| Zhukovsky International Airport (wd:Q2388744)      | 1 (xsd:int) | Zhukovsky International Airport (en)        |
| Zvartnots International Airport (wd:Q1124922)      | 1 (xsd:int) | Zvartnots International Airport (en)        |

I executed a query that finds items that are instance of "international airport" (wd:Q644371) and have exactly one runway (property P529). The query returns the airport IRI, its English label, and the runway count, ordering by label. The query returned 594 airports. The SPARQL query is provided above.

Which international airports have exactly one runway? Please provide their labels and the number of runways, and order by the airport label.

[System]
You are a question answering assistant. Your job is to generate a SPARQL query to answer a given user question.

You have access to the following knowledge graphs:
wikidata at https://qlever.cs.uni-freiburg.de/api/wikidata-sebastian

You are provided with some general notes:
No notes available

You can use the following SPARQL prefixes implicitly in all functions:
cc: <http://creativecommons.org/ns#
data: <https://www.wikidata.org/wiki/Special:EntityData/
dct: <http://purl.org/dc/terms/
geo: <http://www.opengis.net/ont/geosparql#
geof: <http://www.opengis.net/def/function/geosparql/
imdb: <https://www.imdb.com/
math: <http://www.w3.org/2005/xpath-functions/math#
ontolex: <http://www.w3.org/ns/lemon/ontolex#
owl: <http://www.w3.org/2002/07/owl#
p: <http://www.wikidata.org/prop/
pq: <http://www.wikidata.org/prop/qualifier/
pqn: <http://www.wikidata.org/prop/qualifier/value-normalized/
pqv: <http://www.wikidata.org/prop/qualifier/value/
pr: <http://www.wikidata.org/prop/reference/
prn: <http://www.wikidata.org/prop/reference/value-normalized/
prov: <http://www.w3.org/ns/prov#
prv: <http://www.wikidata.org/prop/reference/value/
ps: <http://www.wikidata.org/prop/statement/
psn: <http://www.wikidata.org/prop/statement/value-normalized/
psv: <http://www.wikidata.org/prop/statement/value/
qfn: <http://qlever.cs.uni-freiburg.de/function#
ql: <http://qlever.cs.uni-freiburg.de/builtin-functions/
qlss: <https://qlever.cs.uni-freiburg.de/spatialSearch/
rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#
rdfs: <http://www.w3.org/2000/01/rdf-schema#
ref: <http://www.wikidata.org/reference/
s: <http://www.wikidata.org/entity/statement/
schema: <http://schema.org/
skos: <http://www.w3.org/2004/02/skos/core#
v: <http://www.wikidata.org/value/
wd: <http://www.wikidata.org/entity/
wdno: <http://www.wikidata.org/prop/novalue/
wdt: <http://www.wikidata.org/prop/direct/
wdtn: <http://www.wikidata.org/prop/direct-normalized/
wikibase: <http://wikiba.se/ontology#
xsd: <http://www.w3.org/2001/XMLSchema#
foaf: <http://xmlns.com/foaf/0.1/
dc: <http://purl.org/dc/elements/1.1/
gn: <http://www.geonames.org/ontology#
bd: <http://www.bigdata.com/rdf#
hint: <http://www.bigdata.com/queryHints#
qb: <http://purl.org/linked-data/cube#
void: <http://rdfs.org/ns/void#

You should follow a step-by-step approach to generate the SPARQL query:
1. Determine possible entities and properties implied by the user question.
2. Search for the entities and properties in the knowledge graphs. Where applicable, constrain the searches with already identified entities and properties.
3. Gradually build up the SPARQL query using the identified entities and properties. Start with simple queries and add more complexity as needed. Execute intermediate queries to get feedback and to verify your assumptions. You may need to refine or rethink your current plan based on the query results and go back to step 2 if needed, possibly multiple times.
4. Use the answer or cancel function to finalize your answer and stop the generation process.

Additional rules:
1. Explain your thought process before and after each step and function call.
2. Do not just use or make up entity or property identifiers without verifying their existence in the knowledge graphs first.
3. Always execute your final SPARQL query before giving an answer to make sure it returns the expected results.
4. The SPARQL query should always return the actual identifiers / IRIs of the items in its result. It additionally may return labels or other human-readable information, but they are optional and should be put within optional clauses unless explicitly requested by the user.
5. Do not stop early if there are still obvious improvements to be made to the SPARQL query. For example, keep refining your SPARQL query if its result contains irrelevant items or is missing items you expected.
6. Do not perform additional computation (e.g. filtering, sorting, calculations) on the result of the SPARQL query to determine the answer. All computation should be done solely within SPARQL.
7. For questions with a "True" or "False" answer the SPARQL query should be an ASK query.
8. Do not use 'SERVICE wikibase:label { bd:serviceParam wikibase:language ...' in SPARQL queries. It is not SPARQL standard and unsupported by the used QLever SPARQL endpoints. Use rdfs:label or similar properties to get labels instead.

[User] Which international airports have exactly one runway? Please provide their labels and the number of runways, and order by the airport label.

[Assistant] Step 1: I'll search for relevant entities and properties: "international airport", "airport", and properties related to "runway", "number of runways", or items for "runway". I'll start by searching entities and properties. I'll explain each step and show the intermediate findings. I'll run searches against the Wikidata endpoint.

[Tool]
Top 10 entity alternatives:
1. international airport (wd:Q644371)
2. International Airport (wd:Q55614529): 1985 film directed by Don Chaffey, instance of film
3. Domodedovo International Airport, also known as Moscow Domodedovo Airport, Moscow-Domodedovo Airport, Moscow International Airport, UUDD, DME (wd:Q186614): international airport serving Moscow, Russia, instance of commercial traffic aerodrome, instance of international airport, instance of federal aeroport
4. John F. Kennedy International Airport, also known as JFK International Airport, Idlewild International Airport, Kennedy International, Kennedy International Airport, KJFK (wd:Q8685): international airport in New York, United States, instance of commercial traffic aerodrome, instance of international airport
5. Sheremetyevo International Airport, also known as Sheremetyevo Airport, SVO, UUEE, Moscow Airport, Moscow International Airport (wd:Q192733): international airport serving Moscow, Russia, instance of commercial traffic aerodrome, instance of international airport, instance of federal aeroport, instance of airport
6. Istanbul-Atatürk, also known as LTBA, Istanbul Atatürk Airport, Istanbul Ataturk Airport, İstanbul Atatürk, Atatürk International Airport (wd:Q86473): international airport in Istanbul, Turkey, instance of commercial traffic aerodrome, instance of international airport
7. Dubai International Airport, also known as DXB, OMDB (wd:Q193439): international airport in Dubai, United Arab Emirates, instance of commercial traffic aerodrome, instance of international airport
8. Singapore Changi Airport, also known as Changi Airport, WSSS, SIN, Changi International Airport, Changi Airport Singapore (wd:Q32159): instance of commercial traffic aerodrome, international airport in Singapore, instance of international airport, instance of airport
9. Tokyo International Airport, also known as Haneda Airport, RJTT, HND (wd:Q204853): international airport serving Tokyo, Japan, instance of commercial traffic aerodrome, instance of international airport, instance of airport
10. Narita International Airport, also known as Narita Airport, 成田, Tokyo Narita Airport, RJAA, NRT (wd:Q36454): instance of commercial traffic aerodrome, instance of international airport, airport serving Tokyo, Japan, instance of airport

[Assistant] I'll search for properties related to "runways" or "number of runways".

[Tool]
Top 10 property alternatives:
1. runway, also known as RWY (wd:P529 as psv/psn/prn/prv/pqn/p/pq/wdtn/wdt/pqv/ps/pr): name (direction) of runway at airport/airfield/airstrip
2. area, also known as acreage, total area, surface area, surface, size (wd:P2046 as psv/psn/prn/prv/pqn/p/pq/wdtn/wdt/pqv/ps/pr): area occupied by an object
3. UTC date of spacecraft landing, also known as date of spacecraft landing, time of spacecraft landing, spacecraft landing time, landing time, landing date (wd:P620 as psv/psn/prn/prv/pqn/p/pq/wdtn/wdt/pqv/ps/pr): date of spacecraft landing in UTC
4. takeoff and landing capability (wd:P1956 as psv/psn/prn/prv/pqn/p/pq/wdtn/wdt/pqv/ps/pr): type of takeoff and landing the aircraft is designed to operate
5. wingspan (wd:P2050 as psv/psn/prn/prv/pqn/p/pq/wdtn/wdt/pqv/ps/pr): distance from one wingtip to the other, of an airplane or an animal
6. number of platform tracks, also known as Tracks, platform tracks (wd:P1103 as psv/psn/prn/prv/pqn/p/pq/wdtn/wdt/pqv/ps/pr): number of tracks served by the platforms at a railway station
7. tracklist, also known as list of tracks, songs, track list, track listing, tracks (wd:P658 as psv/psn/prn/prv/pqn/p/pq/wdtn/wdt/pqv/ps/pr): audio tracks contained in this release, subproperty of has part(s)
8. axle track, also known as track, wheel track (wd:P8597 as psv/psn/prn/prv/pqn/p/pq/wdtn/wdt/pqv/ps/pr): distance between the centerline of two roadwheels on the same axle
9. location of landing, also known as landing site, landing location (wd:P1158 as psv/psn/prn/prv/pqn/p/pq/wdtn/wdt/pqv/ps/pr): subproperty of significant place, location where the craft landed, subproperty of location
10. aerodrome reference point, also known as ARP, airport reference point, airfield reference point (wd:P2786 as psv/psn/prn/prv/pqn/p/pq/wdtn/wdt/pqv/ps/pr): official coordinates for airport. Aeronautical publications use format with degrees, minutes and seconds concatenated, subproperty of coordinate location

[Tool]
Top 10 property alternatives:
1. runway, also known as RWY (wd:P529 as psv/psn/prn/prv/pqn/p/pq/wdtn/wdt/pqv/ps/pr): name (direction) of runway at airport/airfield/airstrip
2. ICAO airport code, also known as ICAO aerodrome code, ICAO airfield code, International Civil Aviation Organization airport code (wd:P239 as psv/psn/prn/prv/pqn/p/pq/wdtn/wdt/pqv/ps/pr): four-character alphanumeric identifier for designating airports (for airlines, see P230)
3. aerodrome reference point, also known as ARP, airport reference point, airfield reference point (wd:P2786 as psv/psn/prn/prv/pqn/p/pq/wdtn/wdt/pqv/ps/pr): official coordinates for airport. Aeronautical publications use format with degrees, minutes and seconds concatenated, subproperty of coordinate location
4. number of lanes, also known as number of lanes of that road in a direction, lanes number (wd:P10367 as psv/psn/prn/prv/pqn/p/pq/wdtn/wdt/pqv/ps/pr): number of lanes on this road in a given direction
5. number of platform tracks, also known as Tracks, platform tracks (wd:P1103 as psv/psn/prn/prv/pqn/p/pq/wdtn/wdt/pqv/ps/pr): number of tracks served by the platforms at a railway station
6. number of branches, also known as branches, number of locations, number of shops, number of stores, number of supermakerts (wd:P8368 as psv/psn/prn/prv/pqn/p/pq/wdtn/wdt/pqv/ps/pr): number of branches or stores affiliated with a chain, brand or company
7. image of design plans, also known as floor plan, plan view image, plan view, plan image, design plan (wd:P3311 as psv/psn/prn/prv/pqn/p/pq/wdtn/wdt/pqv/ps/pr): image representing the plan of a building or place, subproperty of detail map
8. takeoff and landing capability (wd:P1956 as psv/psn/prn/prv/pqn/p/pq/wdtn/wdt/pqv/ps/pr): type of takeoff and landing the aircraft is designed to operate
9. wheelbase, also known as wheelbases (wd:P3039 as psv/psn/prn/prv/pqn/p/pq/wdtn/wdt/pqv/ps/pr): horizontal distance between the centers of the front and rear wheels
10. number of parts of this work, also known as number of movements, number of parts of this work of art, number of pictures, number of segments of anthology film, number of songs (wd:P2635 as psv/psn/prn/prv/pqn/p/pq/wdtn/wdt/pqv/ps/pr): the number of parts that a work comprises

[Assistant] I'll run a SPARQL query that finds items that are instance of "international airport" (wd:Q644371) and counts distinct runways (wdt:P529). I'll return items with exactly one runway and include English labels, ordering by the label. I'll execute the query now.

[Tool]
Got 594 rows and 3 columns, showing the first 5 and last 5 rows below:
| airport                                            | runwayCount | label                                       |
| -------------------------------------------------- | ----------- | ------------------------------------------- |
| Abakan International Airport (wd:Q558204)          | 1 (xsd:int) | Abakan International Airport (en)           |
| Abel Santamaría Airport (wd:Q3496664)              | 1 (xsd:int) | Abel Santamaría Airport (en)                |
| Abraham González Airport (wd:Q2233076)             | 1 (xsd:int) | Abraham González Airport (en)               |
| Adana Şakirpaşa Airport (wd:Q1430728)              | 1 (xsd:int) | Adana Şakirpaşa Airport (en)                |
| Addis Ababa Bole International Airport (wd:Q44923) | 1 (xsd:int) | Addis Ababa Bole International Airport (en) |
| ...                                                | ...         | ...                                         |
| Zamboanga International Airport (wd:Q1200033)      | 1 (xsd:int) | Zamboanga International Airport (en)        |
| Zaporizhzhia International Airport (wd:Q1432786)   | 1 (xsd:int) | Zaporizhzhia International Airport (en)     |
| Zenata – Messali El Hadj Airport (wd:Q2875969)     | 1 (xsd:int) | Zenata – Messali El Hadj Airport (en)       |
| Zhukovsky International Airport (wd:Q2388744)      | 1 (xsd:int) | Zhukovsky International Airport (en)        |
| Zvartnots International Airport (wd:Q1124922)      | 1 (xsd:int) | Zvartnots International Airport (en)        |

[Assistant] I'll run the final SPARQL query that finds items that are instance of "international airport" (wd:Q644371), counts distinct runways via property P529, filters to those with exactly one runway, returns the airport IRI, English label, and runway count, and orders the results by the label. Then I'll report the number of results and provide the query.

[Tool]
Got 594 rows and 3 columns, showing the first 5 and last 5 rows below:
| airport                                            | runwayCount | label                                       |
| -------------------------------------------------- | ----------- | ------------------------------------------- |
| Abakan International Airport (wd:Q558204)          | 1 (xsd:int) | Abakan International Airport (en)           |
| Abel Santamaría Airport (wd:Q3496664)              | 1 (xsd:int) | Abel Santamaría Airport (en)                |
| Abraham González Airport (wd:Q2233076)             | 1 (xsd:int) | Abraham González Airport (en)               |
| Adana Şakirpaşa Airport (wd:Q1430728)              | 1 (xsd:int) | Adana Şakirpaşa Airport (en)                |
| Addis Ababa Bole International Airport (wd:Q44923) | 1 (xsd:int) | Addis Ababa Bole International Airport (en) |
| ...                                                | ...         | ...                                         |
| Zamboanga International Airport (wd:Q1200033)      | 1 (xsd:int) | Zamboanga International Airport (en)        |
| Zaporizhzhia International Airport (wd:Q1432786)   | 1 (xsd:int) | Zaporizhzhia International Airport (en)     |
| Zenata – Messali El Hadj Airport (wd:Q2875969)     | 1 (xsd:int) | Zenata – Messali El Hadj Airport (en)       |
| Zhukovsky International Airport (wd:Q2388744)      | 1 (xsd:int) | Zhukovsky International Airport (en)        |
| Zvartnots International Airport (wd:Q1124922)      | 1 (xsd:int) | Zvartnots International Airport (en)        |

[Assistant] Now I'll prepare the final answer including the SPARQL query and the result summary. I followed the required step-by-step explanation during the process.

[Tool] Stopped generation process


PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
SELECT ?company ( COUNT( ?item ) AS ?count ) ?companyLabel WHERE {
  ?item wdt:P272 ?company .
  OPTIONAL {
    ?company rdfs:label ?companyLabel .
    FILTER ( LANG( ?companyLabel ) = "en" )
  }
}
GROUP BY ?company ?companyLabel
HAVING ( COUNT( ?item ) > 100 )
ORDER BY DESC ( ?count )

Got 219 rows and 3 columns, showing the first 5 and last 5 rows below:
| company                                   | count          | companyLabel                     |
| ----------------------------------------- | -------------- | -------------------------------- |
| Theater Basel (wd:Q391991)                | 7865 (xsd:int) | Theater Basel (en)               |
| Schauspielhaus Zürich (wd:Q40313234)      | 3203 (xsd:int) | Schauspielhaus Zürich (en)       |
| Vitagraph Studios (wd:Q1418400)           | 2551 (xsd:int) | Vitagraph Studios (en)           |
| Essanay Studios (wd:Q1368540)             | 1974 (xsd:int) | Essanay Studios (en)             |
| Universal Pictures (wd:Q168383)           | 1625 (xsd:int) | Universal Pictures (en)          |
| ...                                       | ...            | ...                              |
| Laika (wd:Q54805138)                      | 103 (xsd:int)  | Laika (en)                       |
| C-mine Cultuurcentrum Genk (wd:Q33100443) | 103 (xsd:int)  | C-mine Cultuurcentrum Genk (en)  |
| TriStar Pictures (wd:Q651454)             | 102 (xsd:int)  | TriStar Pictures (en)            |
| Disney Television Animation (wd:Q1392453) | 102 (xsd:int)  | Disney Television Animation (en) |
| Pixar (wd:Q127552)                        | 101 (xsd:int)  | Pixar (en)                       |

Step-by-step reasoning and actions:

Step 1 — identify property for "production company":
- Before calling the property search: I expected a property linking creative works (films, plays, etc.) to their production company; common Wikidata properties are P272 (production company) and P162 (producer for persons).
- I searched for the property "production company" and found that P272 (production company) is the correct property to use for companies that produced a given work.

Step 2 — verify relevant entity class (optional):
- Before calling the entity search: I checked for entity types like "film production company" to ensure the results are companies. The search showed entities such as Q1762059 (film production company) and Q375336 (film studio), confirming the domain.

Step 3 — build and run aggregate query:
- Before executing: I planned a SPARQL query that counts the number of items (?item) that have wdt:P272 ?company, groups by ?company, filters groups with COUNT(?item) > 100, and orders results by descending count. I also included optional English labels (?companyLabel).
- I executed the query on Wikidata. The query returned 219 companies meeting the criterion (more than 100 items). It returns each company IRI (?company) and the count (?count), with an optional English label when available.

Sample results (top and near-threshold examples from the executed query):
- Top 5 by count:
  - wd:Q391991 — Theater Basel — 7,865
  - wd:Q40313234 — Schauspielhaus Zürich — 3,203
  - wd:Q1418400 — Vitagraph Studios — 2,551
  - wd:Q1368540 — Essanay Studios — 1,974
  - wd:Q168383 — Universal Pictures — 1,625

- Examples near the threshold (just above 100):
  - wd:Q54805138 — Laika — 103
  - wd:Q33100443 — C-mine Cultuurcentrum Genk — 103
  - wd:Q651454 — TriStar Pictures — 102
  - wd:Q1392453 — Disney Television Animation — 102
  - wd:Q127552 — Pixar — 101

Final SPARQL query (returns company IRIs and counts, with optional English labels):
SELECT ?company (COUNT(?item) AS ?count) ?companyLabel WHERE {
  ?item wdt:P272 ?company .
  OPTIONAL { ?company rdfs:label ?companyLabel . FILTER(LANG(?companyLabel) = "en") }
}
GROUP BY ?company ?companyLabel
HAVING (COUNT(?item) > 100)
ORDER BY DESC(?count)

Notes:
- The query returns 219 rows (companies) on the Wikidata endpoint. Each result row contains the company IRI (?company), the count of items they are listed as production company for (?count), and an optional English label (?companyLabel).
- If you want the full ordered list, run the provided SPARQL query against the Wikidata endpoint; it will list all companies with more than 100 items ordered by number of items produced in descending order.

Which production companies have produced more than 100 items, ordered by the number of items produced in descending order?

[System]
You are a question answering assistant. Your job is to generate a SPARQL query to answer a given user question.

You have access to the following knowledge graphs:
wikidata at https://qlever.cs.uni-freiburg.de/api/wikidata-sebastian

You are provided with some general notes:
No notes available

You can use the following SPARQL prefixes implicitly in all functions:
cc: <http://creativecommons.org/ns#
data: <https://www.wikidata.org/wiki/Special:EntityData/
dct: <http://purl.org/dc/terms/
geo: <http://www.opengis.net/ont/geosparql#
geof: <http://www.opengis.net/def/function/geosparql/
imdb: <https://www.imdb.com/
math: <http://www.w3.org/2005/xpath-functions/math#
ontolex: <http://www.w3.org/ns/lemon/ontolex#
owl: <http://www.w3.org/2002/07/owl#
p: <http://www.wikidata.org/prop/
pq: <http://www.wikidata.org/prop/qualifier/
pqn: <http://www.wikidata.org/prop/qualifier/value-normalized/
pqv: <http://www.wikidata.org/prop/qualifier/value/
pr: <http://www.wikidata.org/prop/reference/
prn: <http://www.wikidata.org/prop/reference/value-normalized/
prov: <http://www.w3.org/ns/prov#
prv: <http://www.wikidata.org/prop/reference/value/
ps: <http://www.wikidata.org/prop/statement/
psn: <http://www.wikidata.org/prop/statement/value-normalized/
psv: <http://www.wikidata.org/prop/statement/value/
qfn: <http://qlever.cs.uni-freiburg.de/function#
ql: <http://qlever.cs.uni-freiburg.de/builtin-functions/
qlss: <https://qlever.cs.uni-freiburg.de/spatialSearch/
rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#
rdfs: <http://www.w3.org/2000/01/rdf-schema#
ref: <http://www.wikidata.org/reference/
s: <http://www.wikidata.org/entity/statement/
schema: <http://schema.org/
skos: <http://www.w3.org/2004/02/skos/core#
v: <http://www.wikidata.org/value/
wd: <http://www.wikidata.org/entity/
wdno: <http://www.wikidata.org/prop/novalue/
wdt: <http://www.wikidata.org/prop/direct/
wdtn: <http://www.wikidata.org/prop/direct-normalized/
wikibase: <http://wikiba.se/ontology#
xsd: <http://www.w3.org/2001/XMLSchema#
foaf: <http://xmlns.com/foaf/0.1/
dc: <http://purl.org/dc/elements/1.1/
gn: <http://www.geonames.org/ontology#
bd: <http://www.bigdata.com/rdf#
hint: <http://www.bigdata.com/queryHints#
qb: <http://purl.org/linked-data/cube#
void: <http://rdfs.org/ns/void#

You should follow a step-by-step approach to generate the SPARQL query:
1. Determine possible entities and properties implied by the user question.
2. Search for the entities and properties in the knowledge graphs. Where applicable, constrain the searches with already identified entities and properties.
3. Gradually build up the SPARQL query using the identified entities and properties. Start with simple queries and add more complexity as needed. Execute intermediate queries to get feedback and to verify your assumptions. You may need to refine or rethink your current plan based on the query results and go back to step 2 if needed, possibly multiple times.
4. Use the answer or cancel function to finalize your answer and stop the generation process.

Additional rules:
1. Explain your thought process before and after each step and function call.
2. Do not just use or make up entity or property identifiers without verifying their existence in the knowledge graphs first.
3. Always execute your final SPARQL query before giving an answer to make sure it returns the expected results.
4. The SPARQL query should always return the actual identifiers / IRIs of the items in its result. It additionally may return labels or other human-readable information, but they are optional and should be put within optional clauses unless explicitly requested by the user.
5. Do not stop early if there are still obvious improvements to be made to the SPARQL query. For example, keep refining your SPARQL query if its result contains irrelevant items or is missing items you expected.
6. Do not perform additional computation (e.g. filtering, sorting, calculations) on the result of the SPARQL query to determine the answer. All computation should be done solely within SPARQL.
7. For questions with a "True" or "False" answer the SPARQL query should be an ASK query.
8. Do not use 'SERVICE wikibase:label { bd:serviceParam wikibase:language ...' in SPARQL queries. It is not SPARQL standard and unsupported by the used QLever SPARQL endpoints. Use rdfs:label or similar properties to get labels instead.

[User] Which production companies have produced more than 100 items, ordered by the number of items produced in descending order?

[Assistant] Step 1: I'll search for the relevant property in Wikidata that links an item (a film, TV show, etc.) to its production company (the property is often called "production company"). I'll use the property search first.

[Tool]
Top 10 property alternatives:
1. production company, also known as producer (company), studio, theater company, theatre company, theatrical troupe (wd:P272 as psv/psn/prn/prv/pqn/p/pq/wdtn/wdt/pqv/ps/pr): company that produced this film, audio or performing arts work
2. working title, also known as code name, codename, production name, production title, working name (wd:P1638 as psv/psn/prn/prv/pqn/p/pq/wdtn/wdt/pqv/ps/pr): temporary name of a product or project used during its development
3. produced by, also known as secreted by (wd:P2849 as psv/psn/prn/prv/pqn/p/pq/wdtn/wdt/pqv/ps/pr): links a class of biological features (such as a biochemical compound) with another class of biological entities known to produce it, subproperty of has immediate cause
4. manufacturer, also known as built by, provider, provided by, product of, producer (wd:P176 as psv/psn/prn/prv/pqn/p/pq/wdtn/wdt/pqv/ps/pr): (main or final) manufacturer or producer of this product
5. fabrication method, also known as process used, preparation method, obtained by, method used, method of preparation (wd:P2079 as psv/psn/prn/prv/pqn/p/pq/wdtn/wdt/pqv/ps/pr): method, process or technique used to grow, cook, weave, build, assemble, manufacture the item, subproperty of uses
6. producer, also known as creative producer, film producer, musical producer, music producer, produced by (wd:P162 as psv/psn/prn/prv/pqn/p/pq/wdtn/wdt/pqv/ps/pr): person(s) who produced the film, musical work, theatrical produc...etc.) [for production company, use P272, video games - use P178]
7. location of creation, also known as place made, manufactured in, place built, photographed at, minted at (wd:P1071 as psv/psn/prn/prv/pqn/p/pq/wdtn/wdt/pqv/ps/pr): place where the item was conceived or made; where applicable, location of final assembly, subproperty of significant place
8. product or material produced, also known as products, product or service provided, product, material, or service produced or provided, product, produces (wd:P1056 as psv/psn/prn/prv/pqn/p/pq/wdtn/wdt/pqv/ps/pr): material or product produced by an organization, industry, facility, or process, subproperty of immediate cause of
9. created for, also known as cause of creation, commissioned for, made for, occasion, on the occasion of (wd:P9883 as psv/psn/prn/prv/pqn/p/pq/wdtn/wdt/pqv/ps/pr): a work, event, etc. for which the subject work was created (e.g.... play was created for; a film that a screenplay was written for)
10. production designer, also known as scenic design by, scenic designer (wd:P2554 as psv/psn/prn/prv/pqn/p/pq/wdtn/wdt/pqv/ps/pr): person(s) responsible for overall aesthetic of the story of this...aphics, etc; deals with many and varied logistic related to this, subproperty of film crew member, subproperty of art director

[Assistant] Now I'll search for an item for "film production company" or similar to possibly restrict the results to companies (optional).

[Tool]
Top 10 entity alternatives:
1. film studio, also known as movie studio, movie production company, motion picture studio, film production company (wd:Q375336)
2. film production company, also known as film company, movie company, movie production company (wd:Q1762059)
3. Produksi Film Negara, also known as PFN, State Film Production Company (wd:Q17997316): Indonesian film production company, instance of business
4. pornographic film studio, also known as pornographic film production company, adult video maker (wd:Q30941437)
5. pornography company, also known as adult film production company, pornographic media company, pornographic media studio (wd:Q101040848)
6. Kess film (Production company), also known as Kess film (wd:Q64753003): Production company founded in Berlin by Giulio Baraldi., instance of enterprise, instance of business
7. Pygmalion Production, also known as Pigmalion Prodakshn, Pygmalion Production Film Company (wd:Q108256904): instance of film production company, Russian film production company
8. Oktober, also known as OCTOBER FILM OY, Oktober Oy, Oktober (film production company), October Film (wd:Q125474740): instance of film production company, Finnish film production company
9. Nanjing Film Production Company (wd:Q121298905): instance of production company, production company
10. Russkoe (film production company) (wd:Q55658184): instance of enterprise, instance of business

[Assistant] I'll run an aggregate query that counts items linked via the "production company" property (P272) to each company, and list those with more than 100 items, ordered descending. I'll include labels for readability. Now I execute the SPARQL query.

[Tool]
Got 200 rows and 3 columns, showing the first 5 and last 5 rows below:
| company                                | count          | companyLabel                   |
| -------------------------------------- | -------------- | ------------------------------ |
| Theater Basel (wd:Q391991)             | 7865 (xsd:int) | Theater Basel (en)             |
| Schauspielhaus Zürich (wd:Q40313234)   | 3203 (xsd:int) | Schauspielhaus Zürich (en)     |
| Vitagraph Studios (wd:Q1418400)        | 2551 (xsd:int) | Vitagraph Studios (en)         |
| Essanay Studios (wd:Q1368540)          | 1974 (xsd:int) | Essanay Studios (en)           |
| Universal Pictures (wd:Q168383)        | 1625 (xsd:int) | Universal Pictures (en)        |
| ...                                    | ...            | ...                            |
| Sony Pictures (wd:Q822314)             | 113 (xsd:int)  | Sony Pictures (en)             |
| Village Roadshow Pictures (wd:Q622668) | 112 (xsd:int)  | Village Roadshow Pictures (en) |
| Nea Skini Technis (wd:Q60929381)       | 112 (xsd:int)  | Nea Skini Technis (en)         |
| Finos Film (wd:Q1418294)               | 112 (xsd:int)  | Finos Film (en)                |
| Concertgebouw (wd:Q1867854)            | 111 (xsd:int)  | Concertgebouw (en)             |

[Assistant] I'll run a refined query to retrieve the top 20 production companies (by count of items linked with P272) to present the leading companies and confirm the results.

[Tool]
Got 20 rows and 3 columns, showing the first 5 and last 5 rows below:
| company                                  | count          | companyLabel                     |
| ---------------------------------------- | -------------- | -------------------------------- |
| Theater Basel (wd:Q391991)               | 7865 (xsd:int) | Theater Basel (en)               |
| Schauspielhaus Zürich (wd:Q40313234)     | 3203 (xsd:int) | Schauspielhaus Zürich (en)       |
| Vitagraph Studios (wd:Q1418400)          | 2551 (xsd:int) | Vitagraph Studios (en)           |
| Essanay Studios (wd:Q1368540)            | 1974 (xsd:int) | Essanay Studios (en)             |
| Universal Pictures (wd:Q168383)          | 1625 (xsd:int) | Universal Pictures (en)          |
| ...                                      | ...            | ...                              |
| RKO Pictures (wd:Q267282)                | 937 (xsd:int)  | RKO Pictures (en)                |
| Lubin Manufacturing Company (wd:Q116439) | 922 (xsd:int)  | Lubin Manufacturing Company (en) |
| Lenfilm (wd:Q158478)                     | 917 (xsd:int)  | Lenfilm (en)                     |
| Nestor Film Company (wd:Q3338476)        | 903 (xsd:int)  | Nestor Film Company (en)         |
| Biograph Company (wd:Q465832)            | 874 (xsd:int)  | Biograph Company (en)            |

[Assistant] I'll run a final SPARQL query that lists all companies linked via property P272 (production company) with a count of items > 100, ordered descending. I include the company IRIs and optional English labels. Now I execute that query.

[Tool]
Got 219 rows and 3 columns, showing the first 5 and last 5 rows below:
| company                                   | count          | companyLabel                     |
| ----------------------------------------- | -------------- | -------------------------------- |
| Theater Basel (wd:Q391991)                | 7865 (xsd:int) | Theater Basel (en)               |
| Schauspielhaus Zürich (wd:Q40313234)      | 3203 (xsd:int) | Schauspielhaus Zürich (en)       |
| Vitagraph Studios (wd:Q1418400)           | 2551 (xsd:int) | Vitagraph Studios (en)           |
| Essanay Studios (wd:Q1368540)             | 1974 (xsd:int) | Essanay Studios (en)             |
| Universal Pictures (wd:Q168383)           | 1625 (xsd:int) | Universal Pictures (en)          |
| ...                                       | ...            | ...                              |
| Laika (wd:Q54805138)                      | 103 (xsd:int)  | Laika (en)                       |
| C-mine Cultuurcentrum Genk (wd:Q33100443) | 103 (xsd:int)  | C-mine Cultuurcentrum Genk (en)  |
| TriStar Pictures (wd:Q651454)             | 102 (xsd:int)  | TriStar Pictures (en)            |
| Disney Television Animation (wd:Q1392453) | 102 (xsd:int)  | Disney Television Animation (en) |
| Pixar (wd:Q127552)                        | 101 (xsd:int)  | Pixar (en)                       |

[Tool] Stopped generation process


PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
SELECT DISTINCT ?item ?itemLabel ?object ?objectLabel WHERE {
  {
    ?item wdt:P31 wd:Q11660
  } UNION {
    ?item wdt:P279 wd:Q11660
  }
  .
  ?item wdt:P361 ?object .
  OPTIONAL {
    ?item rdfs:label ?itemLabel FILTER ( lang( ?itemLabel ) = "en" )
  }
  OPTIONAL {
    ?object rdfs:label ?objectLabel FILTER ( lang( ?objectLabel ) = "en" )
  }
}
ORDER BY ?itemLabel

Got 9 rows and 4 columns:
| item                                                        | itemLabel                                        | object                                     | objectLabel                        |
| ----------------------------------------------------------- | ------------------------------------------------ | ------------------------------------------ | ---------------------------------- |
| artificial general intelligence (wd:Q2264109)               | artificial general intelligence (en)             | strong AI and weak AI (wd:Q116852011)      | strong AI and weak AI (en)         |
| artificial intelligence in material science (wd:Q112500260) | artificial intelligence in material science (en) | materials science (wd:Q228736)             | materials science (en)             |
| evolutionary computation (wd:Q1197129)                      | evolutionary computation (en)                    | computational intelligence (wd:Q1122090)   | computational intelligence (en)    |
| machine learning (wd:Q2539)                                 | machine learning (en)                            | artificial intelligence (wd:Q11660)        | artificial intelligence (en)       |
| machine learning (wd:Q2539)                                 | machine learning (en)                            | mathematics (wd:Q395)                      | mathematics (en)                   |
| machine unlearning (wd:Q118970497)                          | machine unlearning (en)                          | artificial intelligence (wd:Q11660)        | artificial intelligence (en)       |
| machine unlearning (wd:Q118970497)                          | machine unlearning (en)                          | mathematics (wd:Q395)                      | mathematics (en)                   |
| open-source artificial intelligence (wd:Q120785614)         | open-source artificial intelligence (en)         | open knowledge (wd:Q2251455)               | open knowledge (en)                |
| open-source artificial intelligence (wd:Q120785614)         | open-source artificial intelligence (en)         | free and open-source software (wd:Q506883) | free and open-source software (en) |

I followed these steps (with explanations before and after each function call):

Step 1 — find the Wikidata item for “artificial intelligence”:
- Action (before): I searched for the entity matching the label “artificial intelligence”.
- Function call: search_entity(kg="wikidata", query="artificial intelligence").
- Result (after): The top match was wd:Q11660 (artificial intelligence). I used this item as the class to find instances/subclasses of.

Step 2 — get items that are instances (P31) or subclasses (P279) of artificial intelligence and that have a "part of" (P361) value:
- Action (before): I ran an initial SPARQL query to retrieve items that are either instances or subclasses of wd:Q11660 and that have a P361 link, requesting English labels.
- Function call: execute with the query
  SELECT ?item ?itemLabel ?object ?objectLabel WHERE {
    { ?item wdt:P31 wd:Q11660 } UNION { ?item wdt:P279 wd:Q11660 } .
    ?item wdt:P361 ?object .
    OPTIONAL { ?item rdfs:label ?itemLabel FILTER (lang(?itemLabel) = "en") }
    OPTIONAL { ?object rdfs:label ?objectLabel FILTER (lang(?objectLabel) = "en") }
  }
  LIMIT 200
- Result (after): That query returned 9 rows. Based on that I refined the query to DISTINCT and added ordering.

Step 3 — final query (verification and final results):
- Action (before): I ran the refined DISTINCT, ordered query to produce final results with IRIs and labels.
- Function call: execute with the final SPARQL query (shown below). The query returns item and object IRIs (and optional English labels).

Final SPARQL query executed:
SELECT DISTINCT ?item ?itemLabel ?object ?objectLabel WHERE {
  { ?item wdt:P31 wd:Q11660 } UNION { ?item wdt:P279 wd:Q11660 } .
  ?item wdt:P361 ?object .
  OPTIONAL { ?item rdfs:label ?itemLabel FILTER (lang(?itemLabel) = "en") }
  OPTIONAL { ?object rdfs:label ?objectLabel FILTER (lang(?objectLabel) = "en") }
}
ORDER BY ?itemLabel

Final results (each line = item IRI (label) — part of object IRI (label)):
- wd:Q2264109 (artificial general intelligence) — part of wd:Q116852011 (strong AI and weak AI)
- wd:Q112500260 (artificial intelligence in material science) — part of wd:Q228736 (materials science)
- wd:Q1197129 (evolutionary computation) — part of wd:Q1122090 (computational intelligence)
- wd:Q2539 (machine learning) — part of wd:Q11660 (artificial intelligence)
- wd:Q2539 (machine learning) — part of wd:Q395 (mathematics)
- wd:Q118970497 (machine unlearning) — part of wd:Q11660 (artificial intelligence)
- wd:Q118970497 (machine unlearning) — part of wd:Q395 (mathematics)
- wd:Q120785614 (open-source artificial intelligence) — part of wd:Q2251455 (open knowledge)
- wd:Q120785614 (open-source artificial intelligence) — part of wd:Q506883 (free and open-source software)

Notes:
- The query returns items that are either P31 (instance of) or P279 (subclass of) wd:Q11660 and that have a P361 (part of) relation to some object. Some items appear with multiple "part of" objects (so they appear multiple times). Labels are provided where available in English.

If you want, I can: (a) include more languages for labels, (b) expand the search to also follow subclass hierarchies recursively (subclasses of subclasses), or (c) include additional properties (e.g., descriptions).

What are the items that are instances or subclasses of artificial intelligence, along with the objects they are part of?

[System]
You are a question answering assistant. Your job is to generate a SPARQL query to answer a given user question.

You have access to the following knowledge graphs:
wikidata at https://qlever.cs.uni-freiburg.de/api/wikidata-sebastian

You are provided with some general notes:
No notes available

You can use the following SPARQL prefixes implicitly in all functions:
cc: <http://creativecommons.org/ns#
data: <https://www.wikidata.org/wiki/Special:EntityData/
dct: <http://purl.org/dc/terms/
geo: <http://www.opengis.net/ont/geosparql#
geof: <http://www.opengis.net/def/function/geosparql/
imdb: <https://www.imdb.com/
math: <http://www.w3.org/2005/xpath-functions/math#
ontolex: <http://www.w3.org/ns/lemon/ontolex#
owl: <http://www.w3.org/2002/07/owl#
p: <http://www.wikidata.org/prop/
pq: <http://www.wikidata.org/prop/qualifier/
pqn: <http://www.wikidata.org/prop/qualifier/value-normalized/
pqv: <http://www.wikidata.org/prop/qualifier/value/
pr: <http://www.wikidata.org/prop/reference/
prn: <http://www.wikidata.org/prop/reference/value-normalized/
prov: <http://www.w3.org/ns/prov#
prv: <http://www.wikidata.org/prop/reference/value/
ps: <http://www.wikidata.org/prop/statement/
psn: <http://www.wikidata.org/prop/statement/value-normalized/
psv: <http://www.wikidata.org/prop/statement/value/
qfn: <http://qlever.cs.uni-freiburg.de/function#
ql: <http://qlever.cs.uni-freiburg.de/builtin-functions/
qlss: <https://qlever.cs.uni-freiburg.de/spatialSearch/
rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#
rdfs: <http://www.w3.org/2000/01/rdf-schema#
ref: <http://www.wikidata.org/reference/
s: <http://www.wikidata.org/entity/statement/
schema: <http://schema.org/
skos: <http://www.w3.org/2004/02/skos/core#
v: <http://www.wikidata.org/value/
wd: <http://www.wikidata.org/entity/
wdno: <http://www.wikidata.org/prop/novalue/
wdt: <http://www.wikidata.org/prop/direct/
wdtn: <http://www.wikidata.org/prop/direct-normalized/
wikibase: <http://wikiba.se/ontology#
xsd: <http://www.w3.org/2001/XMLSchema#
foaf: <http://xmlns.com/foaf/0.1/
dc: <http://purl.org/dc/elements/1.1/
gn: <http://www.geonames.org/ontology#
bd: <http://www.bigdata.com/rdf#
hint: <http://www.bigdata.com/queryHints#
qb: <http://purl.org/linked-data/cube#
void: <http://rdfs.org/ns/void#

You should follow a step-by-step approach to generate the SPARQL query:
1. Determine possible entities and properties implied by the user question.
2. Search for the entities and properties in the knowledge graphs. Where applicable, constrain the searches with already identified entities and properties.
3. Gradually build up the SPARQL query using the identified entities and properties. Start with simple queries and add more complexity as needed. Execute intermediate queries to get feedback and to verify your assumptions. You may need to refine or rethink your current plan based on the query results and go back to step 2 if needed, possibly multiple times.
4. Use the answer or cancel function to finalize your answer and stop the generation process.

Additional rules:
1. Explain your thought process before and after each step and function call.
2. Do not just use or make up entity or property identifiers without verifying their existence in the knowledge graphs first.
3. Always execute your final SPARQL query before giving an answer to make sure it returns the expected results.
4. The SPARQL query should always return the actual identifiers / IRIs of the items in its result. It additionally may return labels or other human-readable information, but they are optional and should be put within optional clauses unless explicitly requested by the user.
5. Do not stop early if there are still obvious improvements to be made to the SPARQL query. For example, keep refining your SPARQL query if its result contains irrelevant items or is missing items you expected.
6. Do not perform additional computation (e.g. filtering, sorting, calculations) on the result of the SPARQL query to determine the answer. All computation should be done solely within SPARQL.
7. For questions with a "True" or "False" answer the SPARQL query should be an ASK query.
8. Do not use 'SERVICE wikibase:label { bd:serviceParam wikibase:language ...' in SPARQL queries. It is not SPARQL standard and unsupported by the used QLever SPARQL endpoints. Use rdfs:label or similar properties to get labels instead.

[User] What are the items that are instances or subclasses of artificial intelligence, along with the objects they are part of?

[Assistant] Step 1: find the Wikidata item for "artificial intelligence". I'll search for the entity first.

[Tool]
Top 10 entity alternatives:
1. artificial intelligence, also known as Intelligent machine, Machines that can think, Machine intelligence, Computational Rationality, Machine understanding (wd:Q11660)
2. A.I. Artificial Intelligence, also known as Artificial Intelligence: A.I., AI, Artificial Intelligence, A.I.: Artificial Intelligence, A.I. (wd:Q221113): 2001 film by Steven Spielberg, instance of film
3. Artificial Intelligence (wd:Q128447): 1985 studio album by John Cale, instance of album
4. Artificial Intelligence (wd:Q4801033): 1992 compilation album; various artists, instance of album
5. Artificial Intelligence (wd:Q2865784): series of albums by Warp Records, instance of album series
6. Artificial Intelligence, also known as Journal of Artificial Intelligence, AIJ (wd:Q4801030): instance of scientific journal, journal published by Elsevier
7. Artificial Intelligence (wd:Q28451730): instance of musical work/composition, song performed by OneRepublic
8. ISO/IEC JTC 1/SC 42: Artificial intelligence, also known as Artificial intelligence (wd:Q98148785): instance of subcommittee of a technical committee, subcommittee of a technical committee
9. Artificial Intelligence (wd:Q125635217): book by Patrick Henry Winston, instance of written work
10. Artificial Intelligence (wd:Q109875730): instance of extended play, 2021 EP by Tom Cardy

[Assistant] I'll run a SPARQL query to find items that are instances (P31) or subclasses (P279) of artificial intelligence (wd:Q11660) and that have a "part of" (P361) value. I'll request English labels as optional fields. Then I'll inspect the results and adjust if needed. Now executing the query.

[Tool]
Got 9 rows and 4 columns:
| item                                                        | itemLabel                                        | object                                     | objectLabel                        |
| ----------------------------------------------------------- | ------------------------------------------------ | ------------------------------------------ | ---------------------------------- |
| evolutionary computation (wd:Q1197129)                      | evolutionary computation (en)                    | computational intelligence (wd:Q1122090)   | computational intelligence (en)    |
| machine unlearning (wd:Q118970497)                          | machine unlearning (en)                          | artificial intelligence (wd:Q11660)        | artificial intelligence (en)       |
| machine learning (wd:Q2539)                                 | machine learning (en)                            | artificial intelligence (wd:Q11660)        | artificial intelligence (en)       |
| artificial general intelligence (wd:Q2264109)               | artificial general intelligence (en)             | strong AI and weak AI (wd:Q116852011)      | strong AI and weak AI (en)         |
| open-source artificial intelligence (wd:Q120785614)         | open-source artificial intelligence (en)         | open knowledge (wd:Q2251455)               | open knowledge (en)                |
| artificial intelligence in material science (wd:Q112500260) | artificial intelligence in material science (en) | materials science (wd:Q228736)             | materials science (en)             |
| machine unlearning (wd:Q118970497)                          | machine unlearning (en)                          | mathematics (wd:Q395)                      | mathematics (en)                   |
| machine learning (wd:Q2539)                                 | machine learning (en)                            | mathematics (wd:Q395)                      | mathematics (en)                   |
| open-source artificial intelligence (wd:Q120785614)         | open-source artificial intelligence (en)         | free and open-source software (wd:Q506883) | free and open-source software (en) |

[Assistant] Now I'll run a final SPARQL query that selects distinct items that are either instances (P31) or subclasses (P279) of artificial intelligence (wd:Q11660), and that have a 'part of' (P361) value. I'll request English rdfs:label values optionally. Then I'll execute the query to verify results.

[Tool]
Got 9 rows and 4 columns:
| item                                                        | itemLabel                                        | object                                     | objectLabel                        |
| ----------------------------------------------------------- | ------------------------------------------------ | ------------------------------------------ | ---------------------------------- |
| artificial general intelligence (wd:Q2264109)               | artificial general intelligence (en)             | strong AI and weak AI (wd:Q116852011)      | strong AI and weak AI (en)         |
| artificial intelligence in material science (wd:Q112500260) | artificial intelligence in material science (en) | materials science (wd:Q228736)             | materials science (en)             |
| evolutionary computation (wd:Q1197129)                      | evolutionary computation (en)                    | computational intelligence (wd:Q1122090)   | computational intelligence (en)    |
| machine learning (wd:Q2539)                                 | machine learning (en)                            | artificial intelligence (wd:Q11660)        | artificial intelligence (en)       |
| machine learning (wd:Q2539)                                 | machine learning (en)                            | mathematics (wd:Q395)                      | mathematics (en)                   |
| machine unlearning (wd:Q118970497)                          | machine unlearning (en)                          | artificial intelligence (wd:Q11660)        | artificial intelligence (en)       |
| machine unlearning (wd:Q118970497)                          | machine unlearning (en)                          | mathematics (wd:Q395)                      | mathematics (en)                   |
| open-source artificial intelligence (wd:Q120785614)         | open-source artificial intelligence (en)         | open knowledge (wd:Q2251455)               | open knowledge (en)                |
| open-source artificial intelligence (wd:Q120785614)         | open-source artificial intelligence (en)         | free and open-source software (wd:Q506883) | free and open-source software (en) |

[Tool] Stopped generation process